Abstract
Biomechanical overload represents one of the main risks in the industrial environment and the main possible source of musculoskeletal disorders and diseases. The aim of the this study is to introduce new technologies for quantitative risk assessment of biomechanical overload, by integrating surface electromyography (sEMG) with an innovative motion-capture system based on inertial measurement units (IMU).
The case study was carried out in collaboration with Fiat Chrysler Automobiles Italy S.p.A. and deals with the analysis of the “central tunnel cabinet assembly” activity, performed by two workers of assembly lines during a working task, which lasts about one minute. The electromyography signals were acquired bilaterally, in three different body regions on the right and on the left side of the Erector Spinae, during standard working activities; the progression of trunk postures (flexion-extension, lateral flexion and twisting) was tracked by using an inertial motion-capture system made of wearable inertial sensors, to evaluate the alignment of the major body segments, using a developed algorithm.
Data analysis showed kinematic and muscular activity patterns consistent with the expected ones. In particular, data show that the proposed technologies can be integrated and simultaneously used during workers’ real performing activities. Data quality also demonstrates that both types of sensors, EMG electrodes and IMU, not influenced each other, neither by electromagnetic noise usually present in an industrial environment. The results of this study show feasibility and usefulness of the integration of kinematic and electromyography technologies for assessing the biomechanical overload in production lines.
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Monaco, M.G.L. et al. (2019). Biomechanical Overload Evaluation in Manufacturing: A Novel Approach with sEMG and Inertial Motion Capture Integration. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds) Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018). IEA 2018. Advances in Intelligent Systems and Computing, vol 818. Springer, Cham. https://doi.org/10.1007/978-3-319-96098-2_88
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DOI: https://doi.org/10.1007/978-3-319-96098-2_88
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